ORIGINAL RESEARCH

Development of system decision support tools for behavioraltrends monitoring of machinery maintenance in a competitiveenvironment

Michael Kanisuru Adeyeri1 • Khumbulani Mpofu1

Received: 5 December 2014 / Accepted: 4 January 2017 / Published online: 17 January 2017

� The Author(s) 2017. This article is published with open access at Springerlink.com

Abstract The article is centred on software system

development for manufacturing company that produces

polyethylene bags using mostly conventional machines in a

competitive world where each business enterprise desires

to stand tall. This is meant to assist in gaining market

shares, taking maintenance and production decisions by the

dynamism and flexibilities embedded in the package as

customers’ demand varies under the duress of meeting the

set goals. The production and machine condition moni-

toring software (PMCMS) is programmed in C# and

designed in such a way to support hardware integration,

real-time machine conditions monitoring, which is based

on condition maintenance approach, maintenance decision

suggestions and suitable production strategies as the

demand for products keeps changing in a highly competi-

tive environment. PMCMS works with an embedded

device which feeds it with data from the various machines

being monitored at the workstation, and the data are read at

the base station through transmission via a wireless trans-

ceiver and stored in a database. A case study was used in

the implementation of the developed system, and the

results show that it can monitor the machine’s health

condition effectively by displaying machines’ health status,

gives repair suggestions to probable faults, decides strategy

for both production methods and maintenance, and, thus,

can enhance maintenance performance obviously.

Keywords Production strategies � Machine monitoring �Maintenance � Decision tools

Introduction

Liliane et al. (2006) opined that industrial manufacturing

systems are characterised by continuous breakdowns with

significant effect on profitability of business enterprise.

These breakdowns which are critical to business success led

to expensive equipment being idled, incurring of losses due

to non-optimisation of labour, ratio of fixed cost to product

output being negatively affected and nonconformance of

output product quality as against the set standard. For man-

ufacturing industries to have competitive advantage over

others, there is need for such industries to understand the

potentials of manufacturing and maintenance. If understood,

proper strategy to exploit such potentials is highly needed.

Strategy calls for the application of senses in adopting an

approach in the right direction and at the right time. It is

bound by integrity, goal and provision of guidance in deci-

sion-making for achievement of objectives (Liliane et al.

2006; Adeyeri and Kareem 2012).

Alhad et al. (2008) made known that the production

system, sometimes, is void of satisfactory overall avail-

ability because of downtime initiated by non-conformance

to set quality standards or excessive machine/component

failures. Initial single station’s mean-time-to-failure

(MTTF) and mean-time-to-repair (MTTR) assessment

could not ascertain the overall system performance, and

dynamic resourcing and customers’ satisfaction were not

addressed in old total productive maintenance (TPM) and

manufacturing execution systems (MES).

Researchers and industrialists have adopted simulation

and optimization as veritable tools to evaluate the

& Michael Kanisuru Adeyeri

adeyerimichaeltut@gmail.com

Khumbulani Mpofu

mpofuk@tut.ac.za

1 Department of Industrial Engineering, Tshwane University of

Technology, Pretoria, South Africa

123

J Ind Eng Int (2017) 13:249–264

DOI 10.1007/s40092-017-0184-z

performance of manufacturing systems (Olafsson and Kim

2002). However, a simple evaluation does not provide

enough details for optimal decision-making.

The works of Papakostas et al. (2010) and Oke and

Charles-Owaba (2006) revealed that the effective man-

agement of maintenance operations is highly important to

decision-makers in the industry as cost could be conserved

in the deployment of labour, strategies for development and

actualization of techniques modelling for efficient main-

tenance implementation and scheduling.

Figure 1 shows the configuration concepts of machines

monitoring and the need for decision-support tools as this

reflects the tasks that require decision-taking on such as

what to produce, how to produce, when to produce and

which maintenance strategy to use in keeping the manu-

facturing enterprise alive from being moribund and to stand

competitively.

A comprehensive predictive maintenance management

program utilizes a combination of the most cost-effective

tools, i.e. thermal imaging, vibration monitoring, tribology,

and other nondestructive testing methods, to obtain the

actual operating condition of critical plant systems, and

based on these factual data, maintenance activities on an

as-required basis are scheduled. Condition-based mainte-

nance or predictive maintenance in a comprehensive

maintenance management program will provide the ability

to optimize the availability of process machinery and

greatly reduce the cost of maintenance.

Condition-based maintenance strategy has recorded

progressive report in recent times characterised by models

and simulation addressing failure prediction problems;

however, germane issues are still not fully integrated to

make this technology result-oriented. Jardine et al. (2006),

Lee et al. (2004) pointed out that enhanced technologies

such as indicators for enhanced monitoring; fast signal

processing algorithms, fast and precise prognostic

approach, new alarm setting, tools for system performance

assessment and degradation have not been developed.

Ni and Yang (2010) proposed a novel mechanism of

intelligent condition monitoring and prognostic system

based on data fusion strategy which is constructed under

the architecture of open system, condition-based monitor-

ing and prognostic modules. The mechanism utilizes fusion

condition, automatic alarm setting, nonlinear prediction

models and assessment. Chryssolouris et al. (2004) pre-

sented pilot implementation approach of human perfor-

mance in virtual manufacturing environments by

employing simulation principles of both virtual reality and

digital mannequin technologies for the performance of

industrial task.

The various maintenance models had been so helpful to

the growth of industrial activities globally. There are still

some limitations or extent to which these models and

simulation techniques could be effectively utilized. Some

of the previous works are considered and their limitations

are spelt out in Table 1.

Despite all the enormous works covered in the afore-

mentioned studies, there is still a missing gap, and the

missing link is that there have not been concerted efforts to

link maintenance decision policy with manufacturing pol-

icy. Also, most of the existing models which either deal

with maintenance or production rarely considered the

Fig. 1 Configuration concept of machines monitoring and the need for decision support tools

250 J Ind Eng Int (2017) 13:249–264

123

effect of competition which is addressed by this paper. The

paper shall be considered under the following sections:

‘‘Model development’’ outlines the methods involved in

developing the production of machines monitoring soft-

ware; the validation and verification of the development

system are enclosed in ‘‘Modelling concept for monitor-

ing’’; and finally, last section summarizes the conclusion of

the work.

Methodology

The following steps are the approaches adopted in

achieving the aim of the research:

1. Model development for manufacturing, maintenance

activities and their linkages;

2. Modelling of monitoring concept;

3. Software development.

Table 1 Comparison of past research models

S/

n

Authors Contributions/summary of previous work done Features of present work over the past researches

1 Lee and

Rosenblatt

(1987)

Presented an optimization model for the case of simultaneous

production cycle and inspection schedule determination

Maintenance and inventory are left on discussed

which is being taken into consideration

2 Albino et al.

(1992)

Study of the effect of maintenance policies on just-in-time

manufacturing

Covers more than JIT, it extends to other

manufacturing strategies

3 Ben-Daya

(1999)

Presented a model for integrated production maintenance and

quality for an imperfect process

Production maintenance and quality for an imperfect

process with epileptic power supply is just a part in

the work

4 Jardine et al.

(1999)

Developed an optimal maintenance program based on vibration

monitoring of critical bearings on machinery

This handles more than bearings, it covers electric

motors as well

5 Yuan and

Chaing

(2000)

Formulated an optimal maintenance policy for a production

system subject to aging and shocks

This is a part in the whole proposed model and purely

dynamic

6 Sterman (2000) Maintenance model covers the aspects affecting the overall

maintenance process and cost, such as worker training, reactive

versus proactive culture

The model did not take care of dynamic strategies in

manufacturing which is a major work in this

research

7 Duffuaa et al.

(2001)

Provided a conceptual model that can be used to develop a

realistic simulation

Dynamic maintenance model forms bulk of the work

8 Zineb and

Chadi (2001)

Establishment of an effective way of modeling complex

manufacturing systems through hierarchical and modular

analysis using stochastic Petri nets and Markov chains

Model takes care of quality competitors with focus

on the up keeping of the production equipment in

order to deliver products with high and consistent

quality

9 Andijani and

Dufuaa

(2002)

Review of various areas such as evaluation of maintenance

policies, organization, staffing, materials management and shut-

down policies

It integrates the business model with both

maintenance and manufacturing model to become a

single model

10 Marquez et al.

(2003)

Provided various maintenance optimization models for repairable

systems

It handles both repairable and non-repairable systems

11 Greasly (2005) Showed that the discrete-event simulation study could be done

through system dynamics

This is supporting the reality of the present approach

with quality competitors embedded

12 Mahantesh

et al. (2008)

Integrated plant maintenance management application based on

artificial intelligence (AI) techniques, mainly knowledge-based

systems

Competition is not part of the AI techniques, and this

is being handled in the present work

13 Papakostas

et al. 2010

Discusses a maintenance decision support framework for

addressing short-term operational maintenance decisions at line

maintenance and for deferring maintenance actions that affect

the aircrafts’ dispatching

The decision support system did not consider market

competitiveness and it is solely for aircraft, while

the present paper addresses competitiveness and

centres on polyethelene bag production

14 Jasper et al.

(2011)

Formulation of empirical postulates regarding the technical

system, managerial system and workforce knowledge

Formulation of models with regards to machines

maintenance and products’ quality

15 Golmakani and

Fattahipour

(2011)

Proposed an approach in which preventive and failure

replacement costs as well as inspection cost are taken into

account to determine the optimal replacement policy and an

age-based inspection scheme

The production and competition were not considered

and these form a part of the work under

consideration

15 Kareem and

Owolabi

(2012)

Formulation of dynamic programming models for optimizing

maintenance planning using Markov chains, transition matrices

This only addresses maintenance, but the present

model incorporates maintenance and production

under competitive environment

J Ind Eng Int (2017) 13:249–264 251

123

Model development

The model development is based on the strategic decisions

for both manufacturing and maintenance activities. Adeyeri

and Kareem (2012) had once formulated models as assis-

tance for carrying out comprehensive maintenance activi-

ties plan as at when due and at the point when machines are

under stress to meeting up with customers’ demand under

competitive environment. The salient parameters needed

for the achievement of the present work shall be discussed.

Under the strategic decisions for manufacturing, the fol-

lowing parameters are considered: demand; market share;

advertising; price changes; inventory; competition; and

mode of operations/production methods, while types of

maintenance, schedule of maintenance and its dynamics

are covered under the strategic maintenance decision.

START

Input market share (MS); Pt, tb, te

Compute Qc; Pt, Pactual; Pi;

Determine Pactual

Is Pt

met?

NOYES

Is 0.2 ≤ μ ≤ 0.5

Is μ > 0.5?

Is μ ≥ 0 5

Initialize Maintenance on the basis of μ values

Use opportunistic maintenance

Use spare part inventory

Use static planned preventive and breakdown maintenance

Use opportunistic, predictive and

condition based maintenance

NO

YES

YES

YES

A

NO

NO

Is performance okay?

NOYES

Fig. 2 Flowchart of dynamic model for strategies in maintenance prediction (Adeyeri et al. 2015)

252 J Ind Eng Int (2017) 13:249–264

123

The flowchart used in the model formulation and mod-

elling linkage of production and maintenance activities is

as expressed in Fig. 2, while the mathematical formulation

of the model can be found in the publication of Adeyeri and

Kareem (2012) and Adeyeri et al. (2015). For clarity and

brevity, the terms expressed in the flowchart are defined as

: Ms (market share); Pt (total output); tb (mean time to

maintain machines i); te (expected running time of machine

(1); Qc (capacity of plant); Pactual (actual production); Pi

(total loss due to maintenance activity); and li (range of

severity).

Modelling concept for monitoring

The monitoring concept is mainly on hardware which

comprises temperature and vibration sensors, AVR

microcontroller (Atmega16; Atmega32); real time clock

NO

Is

actualP demand<, and

0.5µ <

Is

actualP demand=, and 0.5µ <

Is

actualP demand<, and 0.5µ >

Is

actualP demand>, and 0.5µ <

Is

actualP demand> ,

and 0.5µ >

Is

actualP demand= ,

and 0.5µ > NONONO

NO NO

A

Initialize choice of production method and maintenance

JIT, opportunistic or static maintenance strategy is employed

AUTO, breakdown maintenance based on opportunistic and static grouping is preferred

AUTO, preventive, dynamic maintenance and opportunistic grouping is recommended

CON, breakdown maintenance based on static opportunistic grouping is preferred

JIT, use dynamic maintenance strategy based on static and opportunistic grouping

CON, preventive, predictive maintenance with opportunistic and dynamic grouping is recommended

YES

YES

YES YES

YESY

ES

Is performance satisfactory?

STOP

YES

Fig. 2 continued

J Ind Eng Int (2017) 13:249–264 253

123

(RTC); liquid crystal display (LCD), MJ MRF24J40MA

(2.4 GHz ZigBee) transceiver, transformer, capacitor,

resistors, MiKroC board platform, Vero board and other

electrical components. The science behind the concept has

been explained by the authors in an article entitled

‘‘Development of Hardware System using Temperature and

Vibration Maintenance Models Integration Concepts for

Conventional Machines Monitoring’’ (Adeyeri et al. 2016).

The modelling concept is designed to make use of the

design shown in Fig. 3. Hardware is attached to the func-

tioning machines at the workstation and machines data are

transmitted through wireless network arrangement to the

base station for maintenance and production decisions.

The maintenance decision is aided through the model

flowchart of Fig. 4 structured by the authors as found in

(Adeyeri et al. 2016). The acronyms in the flowchart are as

defined here:

Vi?1 (predicted value of the vibration level at next

planned measuring time); ti?1 (elapsed time since damage

is initiated and its development is detected); Vi (current

vibration level value); Zi: (deterioration factor); Vc (critical

vibration level which is to be supplied by the manufac-

turer); Vo (measured vibration before Vi?1); a (gradient by

which the value of the vibration level varied since it started

to deviate from its normal state (Vo) due to initiation of

damage until detection); bi and ci (nonlinear model’s

constants); K (function of loading, speed of machine,

environmental condition); Ei (model error, which is

assumed to be identical, independent and normally dis-

tributed with zero mean and constant variance, N (0, r).The decision to be made is strongly based on this

arrangement coupled with the software provision which is

to be explained in the subsequent section.

Software development

The software model developed was based on the integra-

tion of all the aforementioned methods described above. A

section of the software is designed to monitor machines

Industrial Machines for Polyethylene

Bag production

Workstation Hardware

Base Station

Hardware

Fig. 3 Hardware architecture of the designed concept (Adeyeri et al. 2016)

254 J Ind Eng Int (2017) 13:249–264

123

START

Calculate /Predict Next Vibration Level

Calculate /Predict Next Temperature Value

Input , a, , , , , b

Input , , b, , ,

The Machine Condition

Indicates AbnormalThe Machine Condition

Indicates Normal

Stop

YES YES

NO NO

Fig. 4 Flowchart of condition-based maintenance monitoring (Adeyeri et al. 2016)

Fig. 5 Opening interface form

of the PMCMS

J Ind Eng Int (2017) 13:249–264 255

123

Fig. 6 Flowchart for sub-station module

256 J Ind Eng Int (2017) 13:249–264

123

behavior under various conditions using vibration and

temperature as key factors. The programming was done

using Microsoft Studio Visual C# because of its extensi-

bility. The modules of the software are as discussed below.

Scope of the production and machine condition

monitoring software (PMCMS)

The production and machine condition monitoring soft-

ware (PMCMS) was developed to aid in monitoring the

condition of production machines, suggesting production

strategies to adopt, computing market share values, cost of

production after certain parameters have been specified,

and displaying the status of the machines’ health behavior

over a specified period of time.

PMCMS was designed such that it works with an

embedded device which feeds it with data from the

various machines being monitored. The data are read

and stored in a database. An algorithm is used to pro-

cess the data and the results are displayed as reports

which can be exported to PDF, EXCEL, or WORD

formats. The relationships between the various data

accumulated are plotted as graphs. By containing all

these features, the PMCMS aims to reduce the cost

incurred from consulting external firms for services such

as the business strategy to adopt, determining the cost of

production, computing the plant’s market share, deter-

mining the working state of individual machines and

prediction of the states of the machines over a given

period of time.

Description of PMCMS

The followings are the list of functionalities of the various

windows available on the PMCMS software.

1. Select port page

At the launch of the application, a port selection page is

met. This allows the user to determine the port to which the

hardware is connected. The dropdown menu displays a list

of available ports currently on the system.

2. Home screen

The home screen is the main page of the application. All

other windows are designed as child forms of it. Please

note that if this window is closed, the application exits

completely. The home screen consists of a sub-window that

displays the current reading from the hardware concerning

the status of the machines being monitored. At the top of

the home screen is a menu bar. The menu bar contains

access to a list of other functional pages available for the

application.

The opening interface of the developed software named

production and machine condition monitoring software

(PMCMS) is shown in Fig. 5.

Software development for sub-station and base

station hardware

The embedded code development for the hardware devel-

opment and utilization as embedded system was achieved

through the use of C language on MiKroC platform.

Sub-station and base station flowchart

The flowchart used in coding the temperature sensor and

the vibration sensor to give room for the computer machine

interface under the C language is as shown in Fig. 6. The

base station flowchart for the programming of the hardware

used is as shown in Fig. 7.

Fig. 7 Flowchart for hardware development

J Ind Eng Int (2017) 13:249–264 257

123

Results and discussion

The implementation of the PMCMS is considered in

respect of machines’ production activities and their main-

tenance modules. The PMCMS was used on machines

involved in the production of a polyethylene bag so as to

verify and test the validity of the developed software. This

section shall be considered looking at model implementa-

tion of machines’ production activities and maintenance of

machines based on machine condition monitoring.

Fig. 8 Form for a typical output of PMCMS market share interface for first period

Fig. 9 Form for a typical output of PMCMS market share interface for second period

258 J Ind Eng Int (2017) 13:249–264

123

Table

2Resultsofcase

study

Period/date

Unitsofproduct

produced(m

etric

tons)

Unitsofproduct

dem

anded/sold

(metric

tons)

Cumulativechangein

unitproduct

(metric

tons)

Methodof

productionusedby

thefirm

Model

suggestionon

productionmethod

Maintenance

strategyremark

JIT

CON

AUTO

First

period(16/

04/12to

28/04/

12)

5.4

6.5

1.1

tomeetup

Conventional

(CON)

Yes

No

Yes

Breakdownmaintenance

based

on

opportunisticandstaticgroupingas

l\

0.5

Secondperiod(30/

04/12to

12/05/

12)

5.5

5.2

0.3

Unsold

Conventional

(CON)

No

Yes

No

Breakdownmaintenance

based

on

opportunisticandstatic

groupingisneeded

asl\

0.5

3rd

period(14/05/

12to

26/05/12)

5.2

5.2

0.3

Unsold

Conventional

(CON)

No

Yes

No

Breakdownmaintenance

based

onstatic

opportunisticgroupingisem

ployed

as

l\

0.5

4th

period(28/05/

12to

09/06/12)

5.4

5.0

0.7

Unsold

Conventional

(CON)

No

Yes

No

Breakdownmaintenance

based

onstatic

opportunisticgroupingisem

ployed

as

l\

0.5

5th

period(11/06/

12to

23/06/12)

4.0

5.0

0.3

tomeetup

Conventional

(CON)

Yes

No

Yes

Breakdownmaintenance

based

on

opportunisticandstaticgroupingas

l\

0.5

6th

period(25/06/

12to

07/07/12)

4.4

4.4

0Conventional

(CON)

Yes

No

No

Opportunisticorstatic

maintenance

is

employed

asl\

0.5

JITjust

intime,

CONconventional

process,AUTO

automated

process

J Ind Eng Int (2017) 13:249–264 259

123

Fig. 10 Form for a typical output of vibration data from electric motors

Fig. 11 Form for a typical output of temperature data from electric motors

260 J Ind Eng Int (2017) 13:249–264

123

Model implementation of machines’ production

The production data (production capacity, the workforce

required, quantity of rawmaterials needed), production hours,

amount of product sold and other parameters which had been

defined are supplied to the developed software. Some snap-

shots of samples displaying the software suitability are dis-

played in Fig. 8, and Figure 9 shows the summary of the

results, the various outcomes and possible maintenance sug-

gestions; while Table 2 gives the detailed summary of trans-

actions and the model suggestions on production processes

and the maintenance strategies that need to be adopted as the

demand for the polyethylene bag changes due to its compet-

itiveness under the period of review.

From Table 2 and Fig. 8, in the first period (16/04/

12–28/04/12), the industry sold 5.4 tons of polyethylene

bag produced, but could not meet the demand of 1.1 tons to

the customer. In view of this, the PMCMS gave suggestion

on decisions to take in meeting up for such shortfall by

adopting just-in-time (JIT) or automatic production

approach with the best maintenance decision of ‘‘break-

down maintenance based on opportunistic and static

grouping as l\ 0.5’’. In the second period, with a clue

from Fig. 9, 5.2 tons were demanded for, but the industry’s

production target was 5.5 tons, which implies that 0.3 tons

were left unsold which forms part of the provision for

inventory upkeep in the developed system. Thus, the

PMCMS system gave decision suggestion in support of the

conventional approach of production.

In the sixth period (25/06/12–07/07/12), market demand

was in favour of the industry as total quantities produced

were sold or demanded for. This was corroborated by the

decision given by the PMCMS that JIT approach would be

better for such trend of production.

Fig. 12 Form for a typical output of temperature data from gearbox

J Ind Eng Int (2017) 13:249–264 261

123

The model was able to actualize the set purpose. It

established the appropriate production and maintenance

strategies that could survive the manufacturing industry

under high competition and controllable demands.

PMCMS machines condition monitoring

implementation

As discussed earlier, the modelling concept implementa-

tion is shown in this section. The built hardware as made

known by the authors, Adeyeri et al. (2015), was part of the

system put in place to perfect the PMCMS functionalities.

The hardware when embedded into the machines with the

support platform plugin in the PMCMS, monitoring of the

behavior of the machines, as they are being run, is made

possible. The various vibration and temperature trends

monitored for the gearbox and electric motor of the

machines/plant using the hardware are as shown in

Figs. 10, 11, 12 and 13.

The summary of the vibration data trends read by the

systems developed with the various prognostic measures

and machine conditions is as displayed in Table 3.

As shown in Table 3, on April 16, 2012, the vibration

readings sampled from the production process revealed that

the vibration values of the extruder gearbox, cutting

machine gearbox and punching machine gearbox read

0.970, 0.980 and 0.360 mm/s, respectively. And the

corresponding condition status of these machines indicated

that they are normal (N). On May 14, 2012, 7.5, 1.28 and

1.20 mm/s were recorded for the extruder, cutting machine

and punching machine, respectively. On this note, the

model gave a maintenance suggestion clue that the vibra-

tion abnormality of extruder gearbox is a result of

misalignment and coupling wear. Looking through the

entire Table 3, it has been shown that the model algorithm

is correct as it could distinguish between when the machine

conditions are normal, abnormal, and then display main-

tenance suggestion messages which are valid and result-

oriented.

Conclusion

Conclusively, the right decision on choice of production,

maintenance strategy, pricing and advertisement could be

predicted using this PMCMS with a positive effect on

profitability of the manufacturing system, and ability to

meet customers’ needs. The software was able to assist in

taking appropriate maintenance decision on machinery,

thereby reducing the failure rate and thus correcting

products backlog and enhancing the equipment effective-

ness, management of machinery and products flow.

The study has established that decision on the efficient

and optimum performances of machines lies on the prompt

Fig. 13 Form for a typical output of vibration data from gearbox

262 J Ind Eng Int (2017) 13:249–264

123

and real-time monitoring of the machine components and

behavior; production machines, products demand and

manufacturing processes could be knitted and monitored

under the same module to support maintenance planning;

and functionality with availability of machines is improved

through integrated production and dynamic maintenance

strategies in competitive environment.

Authors’ contributions The collation, documentation and drafting

of the research work was done by Dr. MKA. While Prof KM provided

motivation and editorial services on the article.

Open Access This article is distributed under the terms of the

Creative Commons Attribution 4.0 International License (http://crea

tivecommons.org/licenses/by/4.0/), which permits unrestricted use,

distribution, and reproduction in any medium, provided you give

appropriate credit to the original author(s) and the source, provide a

link to the Creative Commons license, and indicate if changes were

made.

References

Adeyeri MK, Kareem B (2012) Maintenance dynamics tools for

machines functionality in a competitive environment. Ind Eng

Lett 2(7):12–19

Adeyeri MK, Mpofu K, Kareem B (2015) Modelling of product

demand as pointer tool for choice of maintenance strategy. Afr J

Sci Technol Innov Dev 7(2):144–150

Adeyeri MK, Mpofu K, Kareem B (2016) Development of hardware

system using temperature and vibration maintenance models

integration concepts for conventional machines monitoring: a

case study. J Ind Eng Int 12(1):93–109

Albino V, Carella G, Okogbaa O (1992) Maintenance polices in just-

in-time manufacturing lines. Int J Prod Res 30:369–382

Alhad A, Xiaohui C, Jay L, Jun N and Ziming Y (2008) Optimized

maintenance design for manufacturing performance improve-

ment using simulation. In: Proceedings of the 40th Conference

on Winter Simulation Conference, IEEE, pp 1811–1819

Andijani A, Duffuaa S (2002) Critical evaluation of simulation

studies in maintenance systems. Prod Plan Control

13(4):336–341

Ben-Daya M (1999) Integrated production maintenance and quality

model for imperfect processes. IIE Trans 31(6):491–501

Chryssolouris G, Mavrikios D, Fragos D, Karabatsou V, Alexopoulos

K (2004) Analysis of assembly and maintenance processes using

virtual reality and digital mannequin technologies. Virtual

Augment Real Appl Manuf. doi:10.1007/978-1-4471-3873-0_6

Duffuaa SO, Ben-Daya M, Al-Sultan KS, Andijani AA (2001) A

generic conceptual simulation model for maintenance systems.

J Qual Maint Eng 7(3):34–40

Golmakani HR, Fattahipour F (2011) Age-based inspection

scheme for condition-based maintenance. J Qual Maint Eng

17(1):93–110 (Emerald Group Publishing Ltd.)

Table 3 Gearbox vibration readings and maintenance model suggestion decision for extruder, cutting and punching machines

Sampling

dates

Extruder’s gearbox vibration (mm/s),

conditions and decision

Cutting machine gearbox vibration (mm/

s), conditions and decision

Punching machine gearbox vibration

(mm/s), conditions and decision

Vibration

(mm/s)

Conditions Model decision

suggestion on

maintenance

activities

Vibration

(mm/s)

Conditions Model

decision

suggestion on

maintenance

activities

Vibration

(mm/s)

Conditions Model

decision

suggestion on

maintenance

activities

16/4/2012 0.970 N 0.980 N 0.360 N

18/4/2012 0.350 N 0.350 N 0.300 N

28/4/2012 1.380 N 1.300 N 1.280 N

30/4/2012 1.400 N 1.320 N 1.240 N

10/5/2012 1.340 N 1.240 N 1.180 N

14/5/2012 7.500 A Misalignment

and coupling

wear. Stop

machine for

repairs

1.280 N 1.200 N

15/5/2012 0.820 N 1.200 N 1.100 N

18/5/2012 1.100 N 1.250 N 0.880 N

28/5/2012 1.460 N 1.200 N 1.200 N

09/6/2012 2.000 N 1.240 N 2.200 N

11/6/2012 1.220 N 4.700 A Mechanical

looseness,

stop machine

1.800 N

22/62012 1.200 N 2.240 N 1.340 N

25/6/2012 1.800 N 1.880 N 0.960 N

04/7/2012 1.600 N 1.900 N 1.400 N

N normal machine status, A abnormal machine status

J Ind Eng Int (2017) 13:249–264 263

123

Greasly A (2005) Using system dynamics in a discrete-event

simulation study of a manufacturing plant. Int J Oper Prod

Manag 25(6):534–548

Jardine AKS, Joseph T, Benjevic D (1999) Optimising condition-

based maintenance decisions for equipment subject to vibration

monitoring. J Qual Maint Eng 5(3):192–202. (http://www.

emerald-library.com). Accessed June 2012

Jardine AKS, Lin D, Banjevic D (2006) A review on machinery

diagnostics and prognostics implementing condition-based main-

tenance. Mech Syst Signal Process 20(7):1483–1510

Jasper V, Warse K, Hans W (2011) Managing condition-based

maintenance technology, a multiple case study in the process

industry. J Qual Maint Eng 17(1):40–62 [http://www.emerald-

library.com]. Accessed 13 June 2012

Kareem B, Owolabi HA (2012) Optimizing maintenance planning in

the production industry using the markovian approach. Technol J

Eng Res 9(2):46–63

Lee HL, Rosenblatt MJ (1987) Simultaneous determination of

production cycle and inspection schedule in a production system.

Manag Sci 33(9):1125–1136

Lee J, Qiu H, Ni J, Djurdjanovi D (2004) Infortronics technologies

and predictive tools for next-generation maintenance systems.

IN: 11th IFAC Symposium on information control Problems in

Manufacturing

Liliane P, Srinivas KP, Ann V (2006) Evaluating the effectiveness of

maintenance strategies. J Qual Maint Eng 12(12):7–20 (Emer-ald Group Publishing Ltd.)

Mahantesh N, Ramachandra A, Satosh Kumar AN (2008) Artificial

intelligence-based condition monitoring for plant maintenance.

Assem Autom 28(2):143–150

Marquez CA, Gupta JND, Herguedas SA (2003) Maintenance

policies for a production system with constrained production

rate and buffer capacity. Int J Prod Res 41(9):1909–1926

Ni G, Yang B-S (2010) Intelligent condition monitoring and

prognostics system based on data-fusion strategy. Expert Syst

Appl 37:8831–8840

Oke SA, Charles-Owaba OE (2006) An approach for evaluating

preventive maintenance scheduling cost. Int J Qual Reliab

Manag 23(7):847–879

Olafsson S, Kim J (2002) Simulation Optimization. In: Yucesan E,

Chen CH, Snowdon JL, Charnes JM (eds) Proceedings of the

2002 winter simulation conference. Institute of Electrical and

Electronics Engineers Inc., Piscataway, pp 79–84

Papakostas N, Papachatzakis P, Xanthakis V, Mourtzis D, Chrys-

solouris G (2010) An approach to operational aircraft mainte-

nance planning. Decis Support Syst 48(4):604–612. doi:10.1016/

j.dss.2009.11.010

Sterman JD (2000) Business dynamics: systems thinking and

modeling for a complex world. McGraw-Hill, Kingsport

Yuan J, Chaing J (2000) Optimal maintenance policy for a production

system subject to aging and shocks. J QualMaint Eng 6(3):200–216

Zineb S, Chadi S (2001) Maintenance integration in manufacturing

systems: from the modeling tool to evaluation. Int J Flex Manuf

Syst 13(3):267–285 (Springer Netherland)

264 J Ind Eng Int (2017) 13:249–264

123